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dc.contributor.authorAlim-Marvasti, Ali
dc.contributor.authorRomagnoli, Gloria
dc.contributor.authorDahele, Karan
dc.contributor.authorModarres, Hadi
dc.contributor.authorPérez-García, Fernando
dc.contributor.authorSparks, Rachel
dc.contributor.authorOurselin, Sébastien
dc.contributor.authorClarkson, Matthew J
dc.contributor.authorChowdhury, Fahmida
dc.contributor.authorDiehl, Beate
dc.contributor.authorDuncan, John S
dc.date.accessioned2022-07-08T01:04:22Z
dc.date.available2022-07-08T01:04:22Z
dc.date.issued2022
dc.identifier.issn2632-1297
dc.identifier.otherPMC9156627
dc.identifier.other35663381
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338894
dc.description.abstractSemiology describes the evolution of symptoms and signs during epileptic seizures and contributes to the evaluation of individuals with focal drug-resistant epilepsy for curative resection. Semiology varies in complexity from elementary sensorimotor seizures arising from primary cortex to complex behaviours and automatisms emerging from distributed cerebral networks. Detailed semiology interpreted by expert epileptologists may point towards the likely site of seizure onset, but this process is subjective. No study has captured the variances in semiological localizing values in a data-driven manner to allow objective and probabilistic determinations of implicated networks and nodes. We curated an open data set from the epilepsy literature, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, linking semiology to hierarchical brain localizations. A total of 11 230 data points were collected from 4643 patients across 309 articles, labelled using ground truths (postoperative seizure-freedom, concordance of imaging and neurophysiology, and/or invasive EEG) and a designation method that distinguished between semiologies arising from a predefined cortical region and descriptions of neuroanatomical localizations responsible for generating a particular semiology. This allowed us to mitigate temporal lobe publication bias by filtering studies that preselected patients based on prior knowledge of their seizure foci. Using this data set, we describe the probabilistic landscape of semiological localizing values as forest plots at the resolution of seven major brain regions: temporal, frontal, cingulate, parietal, occipital, insula, and hypothalamus, and five temporal subregions. We evaluated the intrinsic value of any one semiology over all other ictal manifestations. For example, epigastric auras implicated the temporal lobe with 83% probability when not accounting for the publication bias that favoured temporal lobe epilepsies. Unbiased results for a prior distribution of cortical localizations revised the prevalence of temporal lobe epilepsies from 66% to 44%. Therefore, knowledge about the presence of epigastric auras updates localization to the temporal lobe with an odds ratio (OR) of 2.4 [CI95% (1.9, 2.9); and specifically, mesial temporal structures OR: 2.8 (2.3, 2.9)], attesting the value of epigastric auras. As a further example, although head version is thought to implicate the frontal lobes, it did not add localizing value compared with the prior distribution of cortical localizations [OR: 0.9 (0.7, 1.2)]. Objectification of the localizing values of the 12 most common semiologies provides a complementary view of brain dysfunction to that of lesion-deficit mappings, as instead of linking brain regions to phenotypic-deficits, semiological phenotypes are linked back to brain sources. This work enables coupling of seizure propagation with ictal manifestations, and clinical support algorithms for localizing seizure phenotypes.
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2632-1297
dc.sourcenlmid: 101755125
dc.subjectPhenotype
dc.subjectEpilepsy Surgery
dc.subjectPresurgical
dc.subjectCortical Localization
dc.subjectData-driven
dc.titleProbabilistic landscape of seizure semiology localizing values.
dc.typeArticle
dc.date.updated2022-07-08T01:04:22Z
prism.issueIdentifier3
prism.publicationNameBrain Commun
prism.volume4
dc.identifier.doi10.17863/CAM.86301
dcterms.dateAccepted2022-05-16
rioxxterms.versionofrecord10.1093/braincomms/fcac130
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidAlim-Marvasti, Ali [0000-0002-7811-0344]
dc.contributor.orcidDuncan, John S [0000-0002-1373-0681]
dc.identifier.eissn2632-1297
cam.issuedOnline2022-05-19


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International